This paper studies the joint time and power allocation in wireless powered communication networks. Unlike the conventional interference-free model in the time-divisionmultiple-access harvest-then-transmit framework, we propose a novel transmission framework that allows user equipments (UEs) to send data information concurrently in a time splitting manner, which causes mutual interference among UEs. We investigate the system feasibility for the proposed model with both the time and energy constraints. For time feasibility, we investigate the sum of time resource with given data demand and harvest time. For energy feasibility, we focus on the ratio of transmission energy and harvested energy when the data demand and the transmission time is fixed. As an exemplary application of the proposed model, we investigate the minimum throughput maximization problem, which aims at achieving high data rates but also ensures fairness among UEs. Furthermore, we derive the optimal time allocation for information transmission and energy harvesting, and derive a local optimal solution that satisfies the KKT conditions for the transmit power of UEs to the nonconvex and NP-hard sum rate problem. Numerical results are presented to illustrate the theoretical findings and to show the advantage of our proposed schemes compared to previous protocols.

This paper presents novel methods for computing fixed points of positive concave mappings and for characterizing the existence of fixed points. These methods are particularly important in planning and optimization tasks in wireless networks. For example, previous studies have shown that the feasibility of a network design can be quickly evaluated by computing the fixed point of a concave mapping that is constructed based on many environmental and network control parameters such as the position of base stations, channel conditions, and antenna tilts. To address this and more general problems, given a positive concave mapping, we show two alternative but equivalent ways to construct a matrix that is guaranteed to have spectral radius strictly smaller than one if the mapping has a fixed point. This matrix is then used to build a new mapping that preserves the fixed point of the original positive concave mapping. We show that the standard fixed point iterations using the new mapping converges faster than the standard iterations applied to the original concave mapping. As exemplary applications of the proposed methods, we consider the problems of power and load estimation in networks based on the orthogonal frequency division multiple access (OFDMA) technology.

In this paper, we propose a mathematical framework for downlink heterogeneous networks (HetNets) involving cell load factors, which measure the average resource consumption. Due to mutual interference, the cell load levels of different tiers are in general coupled with each other in a nonlinear manner. By leveraging tools from stochastic geometry, we compute the average cell load levels across tiers via fixed-point equations that are dependent upon network parameters (e.g., deployment density, transmit power, association bias) and users' data demands. As an exemplary application of the proposed model, the effect of the association bias on achieving balanced network loads is investigated in two-tier networks. The optimum association bias factor, although not expressed in closed form, can be found efficiently through the standard bisection search method. Numerical results show that in the feasible load region, the previous cell load estimation based on worst-case SIR (signal-to-interference ratio) gives an upper bound on the cell load levels derived in this paper. In addition, the effectiveness of the proposed optimum bias adaptation for the purpose of load balancing is demonstrated by simulation.

In this paper, we consider, joint power and time allocation for wireless powered communication networks (WPCNs). We propose a novel system model that allows users to first harvest energy from the power station (PS) in downlink, and then concurrently send data information to their access points (APs) in uplink. For fixed users' data demands, we provide an iterative algorithm to compute the transmit power and time of each user by using the properties of standard interference mappings. As an exemplary application of the proposed model, we further investigate the minimum throughput maximization problem, which aims at achieving high data rates but also ensures fairness among users. Numerical results are presented to illustrate the theoretical findings and to show the advantage of our proposed model compared to previous protocols.

With Massive MIMO installed on High Altitude Platforms (HAPs), capacity analysis is conducted for both sparse users and hotspot users. Sparse users are assumed to follow the Poisson Point Process and their capacities are obtained via the random geometry theorem. For hotspot users, Massive MIMO combined with HAP-based communication is shown to be able to achieve the multiplexing gain thus increase the hotspot capacity. The channel correlation model of UPA under LOS propagations is obtained for users within the hotspot. An upper bound of the correlation function is further derived to show that the correlation between hotspot users can be sufficiently low, resulting in hotspot capacity improvement. The hotspot capacity is affected by location distributions of the scheduled users. Four user location distribution models are considered, which leads to the capacity upper bound, the practical schemes to implement and the capacity estimation method respectively.